Advances in affordable technologies for sensing and communication have allowed humans to gather data about large distributed infrastructures such as road networks in real-time. The collected data is digested to generate information that is useful for the end users (namely commuters) as well as the road network administrators. Most of this effort falls under broad umbrella of short term traffic forecasting. From the commuters' perspective, travel time is perhaps the most useful information. Predicting travel time along various routes in advance with good accuracy allows commuters to plan their trips appropriately by identifying and avoiding congested roads. Travel time estimation helps transport managers too by helping them identify operational problems such as congestion hot-spots, out-of-sync traffic lights and unhelpful lane directions settings.
Crowd-sourcing based applications allow commuters to predict their travel times along multiple routes. While the prediction accuracy of such applications is reasonable in many instances, they may not be helpful for all vehicles. In certain countries, vehicles larger than cars—such as small commercial trucks are restricted to specific lanes with their own different (often lower) speed limit. Hence, the travel times and congestions seen by such vehicles could be different from the (possibly average) values that are predicted from the crowdsourced data. A variety of data driven techniques to predict travel time have also been proposed based on linear regression, time series models, neural networks, and regression trees to name a few. Most of these methods address prediction in a freeway context. This is mainly because freeways are relatively well instrumented with sensors like loop detectors, AVI detectors, and cameras.
On the other hand, urban/arterial roads have been relatively less studied. A possible reason for this could be the complexities involved in handling traffic lights and intersections. Recently, approaches based on dynamic Bayesian networks (DBN) have also been proposed to predict travel time on arterial roads based on sparse probe vehicle data. Under real world traffic conditions, such sophisticated techniques have been shown to significantly outperform other simpler methods.
Existing approaches either capture dependencies in a detailed manner or in an oversimplified fashion. In one of the approaches the modelling assumption leads to a computationally prohibitive number of parameters to be learnt. This method hence suffers from severe over-fitting problems. In another approach (very efficient in the number of parameters), it is assumed that the state of congestion in a given road is influenced equally by the state of congestion of all its neighbors, which can be very restrictive. In reality, different neighbors will exert different degrees of influence on a given road, for instance, the state of a downstream road which receives bulk of the traffic from an upstream road will exert a higher influence on the congestion state of the upstream road than other neighbors.
In order to overcome this difficulty there has been interest in studying an alternative DBN based approach that strikes a balance by modelling the variations in the degrees of influence a given road may experience from its neighbors, while keeping a check on the number of parameters to be learnt.